Advances in technology have propagated the usage of computers. As computers become more affordable and the internet becomes an integral part of everyday life, many daily functions (e.g., correspondence, shopping, etc.) may be conducted electronically. The shift to an electronic environment has enabled more individuals and businesses to perform daily functions more efficiently.
In order to facilitate communications electronically, electronic signatures is gaining popularity as a method of identification. To protect against fraud, the computer industry has spent time and resources to develop tools and methods for validating and verifying electronic signatures.
Usually, electronic signature is an image. A common method for signature verification is by identifying similarity between images by analyzing the geometric objects and/or image vectors. In an example, the initial time an electronic signature image is received for a person, a file of geometric objects and/or images vectors may be created. This file may represent the electronic signature image and may be employed as a comparison against future incoming electronic signature images for the same person. As discussed herein, geometric objects refer to the different shapes that make up an image. Also, as discussed herein, image vectors refer to the positioning of the object, in other words, the degree and the distances of the objects.
In analyzing the geometric objects and/or image vectors of an electronic signature image, the image raw data may have to be retrieved multiple times in order to identify the geometric objects and the relationship between the geometric objects. This method of analyzing geometric objects and/or image vectors may be a long and tedious process that may require heavy CPU processing. In other words, this method requires the content of the image to be identified. Further, each aspect (e.g., the geometric shape, the positioning, the degree, the distance, etc.) of the image has to be analyzed.